1. Field of the Invention
The present invention relates to a neural system of classification and to a classification method using a system such as this.
In the field of artificial intelligence, neural networks designate techniques that draw inspiration from the workings of the brain to resolve problems of recognition, classification or optimization. In the field of electronic or optic circuits, they designate a certain type of circuit capable of carrying out transformations on vectors having a large number of components.
2.Description of the Prior Art
A known way of classifying objects represented by electric signals and, more precisely, by vectors of signals applied to the inputs of neural networks lies in the implementation of a so-called learning phase. This phase generally consists of the configuration (namely the programming) of a network, also called a classifier, that fulfills a function of performing the envisaged classification as efficiently as possible by using a set of signals, called a learning base, where the membership of each of these signals in on of the classes in which i is desired to classify them is known. This method is known as supervised learning or learning with teacher.
There are several possible methods of encoding classes to be recognized on the output cells of a neural system (or classification system). In one widely used system, a class is assigned to each of the output cells of the system. The class that will be assigned to the object presented in classification mode will be the one corresponding to that cell which has the greatest output (rule of the maximum). This method is very attractive in its simplicity and in the intuitive aspect related thereto (the rule of the maximum). However, it may have major limitations: this type of encoding may increase the complexity of the problem to be resolved.
This type of encoding may lead to a non-linearly separable approach (of the XOR function type) while there could be a linearly separable type of approach.
The convergence time, during the learning, of a classification algorithm (for example the algorithm of retropropagation of the gradient for the most frequently used neural architecture) is a function of the complexity of the systems. For example, for a linearly separable problem, there can be only about ten iterations while about a hundred iterations are necessary, on an average, for the learning of the XOR logic function. This stresses the importance of the encoding of the output cells of the neural or classification system for the learning process.
However, while a high-performance encoding is used for the learning, in the recognition or classification stage it is often useful to be able to apply the rule of the maximum which notably enables interpreting the outputs as probabilities that the analyzed object will belong to one of the classes.
It may be noted moreover that the more difficult the task to be performed by the neural or classification system, the more likely is it that the structure of the system will be complicated. This point is an important one, especially for the physical construction of a neural or classification system. Indeed, for example in neural systems, the difficulty of their parallel layout lies in the number of interconnections between neurons. With the presently used technology, there is an incompatibility between speed and the number of interconnections (see "DARPA Neural Network Study", AFCEA International Press, February 1988). Predictions based on mean term and long term projections suggest that components with capacities of the order of 10.sup.9 operations/second will probably not exceed a connection rate of more than 10.sup.6 interconnections. A promising goal therefore is the simplification of the architecture of the neural or classification systems and, more precisely, the reduction in the number of cells that have to be totally interconnected.
The present invention proposes a solution to the problem of the choice of the encoding of information at the output of the neural network, that enables the use of classification by the rule of the maximum.